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Privacy Preserving Machine Learning Use Case Driven by Srinivasa Rao (11 results)
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Privacy-Preserving Machine Learning: A use-case-driven approach to building and protecting ML pipelines from privacy and security threats (Paperback or Softback)
Published by Packt Publishing 5/24/2024, 2024
ISBN 10: 1800564678 ISBN 13: 9781800564671
Language: English
Paperback or Softback. Condition: New. Privacy-Preserving Machine Learning: A use-case-driven approach to building and protecting ML pipelines from privacy and security threats 1.52. Book.
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Privacy-Preserving Machine Learning: A use-case-driven approach to building and protecting ML pipelines from privacy and security threats
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Privacy-Preserving Machine Learning: A use-case-driven approach to building and protecting ML pipelines from privacy and security threats
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Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Gain hands-on experience in data privacy and privacy-preserving machine learning with open-source ML frameworks, while exploring techniques and algorithms to protect sensitive data from privacy breachesKey Features: Understand machine learning privacy risks and employ machine learning algorithms to safeguard data against breaches Develop and deploy privacy-preserving ML pipelines using open-source frameworks Gain insights into confidential computing and its role in countering memory-based data attacks Purchase of the print or Kindle book includes a free PDF Elektronisches BuchBook Description: In an era of evolving privacy regulations, compliance is mandatory for every enterprise Machine learning engineers face the dual challenge of analyzing vast amounts of data for insights while protecting sensitive information This book addresses the complexities arising from large data volumes and the scarcity of in-depth privacy-preserving machine learning expertise, and covers a comprehensive range of topics from data privacy and machine learning privacy threats to real-world privacy-preserving cases As you progress, you'll be guided through developing anti-money laundering solutions using federated learning and differential privacy Dedicated sections will explore data in-memory attacks and strategies for safeguarding data and ML models You'll also explore the imperative nature of confidential computation and privacy-preserving machine learning benchmarks, as well as frontier research in the field Upon completion, you'll possess a thorough understanding of privacy-preserving machine learning, equipping them to effectively shield data from real-world threats and attacksWhat You Will Learn: Study data privacy, threats, and attacks across different machine learning phases Explore Uber and Apple cases for applying differential privacy and enhancing data security Discover IID and non-IID data sets as well as data categories Use open-source tools for federated learning (FL) and explore FL algorithms and benchmarks Understand secure multiparty computation with PSI for large data Get up to speed with confidential computation and find out how it helps data in memory attacksWho this book is for: This comprehensive guide is for data scientists, machine learning engineers, and privacy engineers Prerequisites include a working knowledge of mathematics and basic familiarity with at least one ML framework (TensorFlow, PyTorch, or scikit-learn) Practical examples will help you elevate your expertise in privacy-preserving machine learning techniquesTable of Contents Introduction to Data Privacy, Privacy threats and breaches Machine Learning Phases and privacy threats/attacks in each phase Overview of Privacy Preserving Data Analysis and Introduction to Differential Privacy Differential Privacy Algorithms, Pros and Cons Developing Applications with Different Privacy using open source frameworks Need for Federated Learning and implementing Federated Learning using open source frameworks Federated Learning benchmarks, startups and next opportunity Homomorphic Encryption and Secure Multiparty Computation Confidential computing - what, why and current state Privacy Preserving in Large Language Models.
